Forecasting Zero-Inflated Price Changes with a Markov Switching Mixture Model for Autoregressive and Heteroscedastic Time Series

The weekly change of several German milkbased commodity prices do not only exhibit traditional patterns like mean dependence and volatility clustering, but also a high frequency of zero changes which can not be explained by well known ARIMA-GARCH models. Therefore, we develop a new mixture model which combines the elements of zero-infl ated models common in micro-econometrics and intermittent demand forecasting with a traditional ARIMA(1,1,0)-GARCH(1,1) model. We describe the model components, data generation processes, maximum likelihood estimation techniques and the generation of forecasting distributions and point forecasts by resampling techniques. The model is applied to a low frequency weekly time series of skimmed whey powder (SWP). Competing submodels are selected by likelihood ratio tests and the AIC information criteria. Furthermore, beyond the evaluation of the out-of-sample forecasting performance, several coverage and independence tests are computed